{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting\n",
    "labels = ['Monica', 'Adrian', 'Jared']\n",
    "num = [230, 100, 98] # Note that this does not need to be percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x8b695c0>,\n",
       "  <matplotlib.patches.Wedge at 0x8b69cc0>,\n",
       "  <matplotlib.patches.Wedge at 0x8b78400>],\n",
       " [Text(-0.12889029014495987, 1.0924226714538416, 'Monica'),\n",
       "  Text(-0.6228249215772513, -0.906691301966822, 'Adrian'),\n",
       "  Text(0.8274673515900017, -0.7247742973178813, 'Jared')],\n",
       " [Text(-0.07030379462452356, 0.5958669117020954, '53.7%'),\n",
       "  Text(-0.33972268449668247, -0.49455889198190284, '23.4%'),\n",
       "  Text(0.4513458281400009, -0.3953314349006625, '22.9%')])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.pie(num, labels=labels, autopct='%1.1f%%', colors=['lightblue', 'lightgreen', 'yellow'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Voting Results: Club President', fontdict={'fontsize': 20})\n",
    "plt.pie(num, labels=labels, autopct='%1.1f%%', colors=['lightblue', 'lightgreen', 'yellow'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
